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COUGER / CO-factors associated with Uniquely-bound GEnomic Regions
Identifies protein cofactors that might provide specificity to paralogous transcription factors (TFs). COUGER is a classification-based framework that uses state-of-heart classification algorithms (support vector machines and random forest) with features that reflect the DNA-binding specificities of putative co-factors. The software can be applied to any two sets of genomic regions bound by paralogous TFs, such as regions derived from ChIP-seq experiments.
Computes a similarity metric between two ChIP-seq datasets to quantify chromatin interactions. In contrast to a basic count of overlaps between two Transcription Factor Binding Sites, IntervalStats allows to compute an exact P-value on their similarity metric. This metric is asymmetric and they demonstrate that it can highlight particular behaviour such as "co-factor" function of a protein. For every query interval, this method produces the closest reference interval, the distance between them and P-value. Their method is insensitive to non-biological variation in datasets (peak width for example). Furthermore, IntervalStats similarity computation can be restricted to a set of genomic regions (such as mappable genome, promoters, open chromatin regions). So it can model peak location biases.
scHMM / sparsely correlated Hidden Markov Models
Provides a method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. scHMM is based on an expectation-maximization-type procedure to infer hidden states and other model parameters. This software considers inter-sample correlations in the hidden state inference. Its method is flexible and can be extended to higher-order HMMs by adjoining more covariates in the penalized logistic regression model.
LPCHP / Linear predictive coding histone profile
Allows the capture and comparison of ChIP-seq histone profiles. LPCHP can be used as an alternative to read intensities, its utility may extend beyond ChIP-seq to other next-generation sequencing (NGS) applications. It can be used in identification of enhancer or regulatory regions in the genome. The tool is robust against changes in p, including cases where it was customized to dataset. LPCHP can identify commonalities between different histone modifications.
fCCAC / functional Canonical Correlation Analysis to evaluate Covariance
Provides a functional canonical correlation analysis approach. fCCAC method can be used (i) to evaluate reproducibility, and flag datasets showing low canonical correlations; (ii) or to investigate covariation between genetic and epigenetic regulations, to infer their potential functional correlations. It can also be used for developing new hypothesis about how changes in transcription factor (TFs), chromatin remodelling enzymes, histone marks, RNA binding protein and epitranscriptome can cooperatively dictate the specification of cell function and identity.
MIM / Motif Independent Metric
Calculates an unbiased quantitative measure for DNA sequence specificity. MIM method has extended previous work by further accounting for sequence specificity due to accumulation of weak sequence features. The information can be used as a guide to systematically investigate the regulatory mechanisms for a wide variety of biological processes. By analyzing both simulated and real experimental data, it was found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. The MIM algorithm is implemented in Python and can be freely accessed for download.
TDCA / Time Dependent ChIP-Sequencing Analysis
Facilitates analysis of a wide range of time course (TC) data in an automated manner. TDCA models changes in sequencing coverage of individual loci within TC ChIP-seq, or conceptually related experiments, as a function of time. Several customizable options are available, such as the ability to tune modeling parameters, include genome specific analyses, and specify normalization constants. The software can be applied to obtain insights that are of potential biological importance.
Calculates metrics which assign a level of similarity between ChIP-Seq profiles. similaRpeak implements six pseudometrics specialized in pattern similarity detection to calculate: (1) the ratio between the areas, (2) the difference between the maximal peaks positions, (3) the ratio between the maximal peaks values, (4) the ratio between the intersection area and the total area, (5) the ratio between the intersection area and the total area of two normalized profiles, and (6) the Spearman’s rho statistic between profiles.
Helps in clustering motifs identified from mammalian species genome-wide. MotifOrganizer is a two-stage, divide-conquer-combine clustering scheme able to gather motif collections in the hundreds of thousands. This algorithm allows motifs of variable different widths to be clustered together and is capable of handling large scale input motif sets. Its performance was extending to include parameters that address more aspects of eukaryotic transcriptional regulation.
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